The Fourth Industrial Revolution has gained considerable momentum in recent years thanks to advancements in computer science. Machine learning and artificial intelligence are at the core of these innovative technologies, transforming every industry from healthcare to retail.
What’s machine learning?
It powers plenty of things we use every day like search engines and Netflix recommendations, but what is machine learning, exactly? Simply put, it’s an application of AI that enables computer systems to identify patterns in data without human programming. Using algorithms and advanced analytics capabilities, the computer program can process vast amounts of data, find key indicators, and make predictions or carry out specific tasks. All of this is done without human manipulation, aside from the initial training process.
The algorithms that serve as the foundation of machine learning can be split into two main categories: supervised and unsupervised. Supervised learning algorithms predict future outcomes by applying previous lessons to new data through the use of labeled data. On the other hand, unsupervised learning explores unlabeled data and draws inferences from those datasets to find hidden patterns and structures. Semi-supervised and reinforcement learning algorithms can be used in the machine learning training process as well.
Deep Learning & AutoML
Deep learning is a subset of machine learning that creates artificial neural networks similar to the human brain. This technology has rapidly expanded with the emergence of big data analytics, which creates more complex and layered neural networks. Image classification algorithms, translation capacities, and speech recognition technology can be built into the deep learning model — which can sometimes decode patterns faster than the human mind can. Deep learning applications are endless, from powering AI assistants like Siri and Alexa to identifying a medical diagnosis.
AutoML is a new technology that enables anyone to run machine learning processes through user-friendly software. Previously, only data scientists with years of experience in statistics and coding could achieve this. Now users can upload their data, choose the desired prediction, and the software will run the proper algorithm. This new software is already changing the game in several industries, including international retail.
What are some applications of ML in retail?
Though some large retail chains are closing stores, eCommerce is booming this year as more consumers shop from home. All retail is quickly becoming international retail as online shopping is globalized. People are also more connected on the internet and social media than ever before. All of this means there’s a lot of data being generated, and this data is a goldmine for retail businesses when properly utilized. With machine learning, retailers can cut costs, optimize pricing and supply chains, predict customer behavior, and more. Consumers can enjoy personalized recommendations, custom orders, AI shopping assistants, and a plethora of other options. There are a few scenarios in which machine learning applications can benefit international retail.
Optimizing Pricing and Supply Chains
Dynamic pricing is a great example of machine learning in retail. Using real-time data, deep learning algorithms creates multiple decision trees before combining them into one predictive model that can be used for price optimization. This is the same technology that companies like Airbnb and Uber use to calculate prices for users. It can also be used to evaluate the impact of potential promotions, allowing retailers to implement sales that deliver higher ROIs. Retailers can also reduce waste and errors in inventory management by using machine learning and root cause analysis.
A Harvard study recently showed that deep learning models could even solve the age-old “optimal auction” problem in economic theory. By modeling the auction as a layered neural network, researchers automated the design of multi-item optimal auctions. International auctions are a major part of eCommerce today, as consumers benefit greatly by cutting out the middleman. People can even buy cars in Nigeria straight from U.S. dealer auctions. The customer can apply for financing, get the best price on their new car, and have it shipped right to them. Algorithms can even be used to optimize international shipping routes, which benefits retailers and consumers.
Personalizing Digital Marketing
One of the best examples of machine learning in retail is personalized marketing, which you can already see everywhere. In previous years, data scientists couldn’t process large amounts of data from unstructured sources such as social media interactions. Computer systems can now analyze all of this big data and generate accurate predictions for customer behavior with deep learning algorithms. Predictive analytics can be used to identify target customers, promote sales and personal offers, cross-sell, upsell, and so much more.
Online grocery shopping has seen a massive boost in 2020, and the majority of shoppers say they plan to continue ordering online after the pandemic is over. With impulse sales accounting for 20% of in-store purchases, international grocers had to devise a digital solution for a more immersive online shopping experience that simultaneously boosts basket sizes. Integrated intelligent search, powered by machine learning algorithms and image and content analytics, does exactly that. Grocers who have implemented these next-generation platforms have boosted conversion rates and customer engagement.
These technologies will lend well to the future of augmented reality in retail. For now, consumers can use AI shopping assistants to create a personalized shopping experience. Retailers like Macy’s have already launched their own shopping assistant platform, allowing customers to get help from their smartphones. Built on existing chatbot technology, AI shopping assistants must be able to interpret and respond in natural language, a process enabled by deep learning.
Machine learning, automation, and the Internet of Things are the core technologies driving smart manufacturing. Using IoT sensors and artificial intelligence, devices throughout an entire factory can speak to each other and automate processes. One of the best use cases for this is detecting any weaknesses or anomalies and performing predictive maintenance for any given problem. This boosts efficiency and productivity as well as optimizes the supply chain, resulting in fewer product shortages.
Newer machines are also more precise, less wasteful, and easier to operate with these capabilities built-in. Vape cartridge filling machines are a great example of this. With an ultra-high-speed processor, fully controllable heat settings, and an advanced oil injection system, these zero-waste machines are designed to increase efficiency and accuracy. Using the touchscreen of a vape cartridge filling machine, a user can fill up to 12,000 vape cartridges, pods, or syringes in just eight hours. Retail manufacturers who invest in smart technologies like these will see increased productivity and revenue.
The Future of International Retail
If you think retail is changing now, just wait. In the years to come, these technologies are only going to become more advanced. Though deep learning probably won’t ever be as powerful as the human brain, artificial neural networks will become far more complex and sophisticated. 3D printing technology will enable affordable mass customization, allowing consumers to get the exact product they desire while cutting retail manufacturers’ operational costs.
AI shopping assistants will be aided by augmented reality to deliver a completely immersive retail experience. Consumers will be able to point their phone at an item on the shelf, read the product description and price, and even see what it’ll look like in their home before they buy it. This futuristic illustration is even closer than you think, as the challenges of this global pandemic have only accelerated technological innovation.